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Cell cycle regulation is critical to blood vessel formation and function, but how the endothelial cell cycle integrates with vascular regulation is not well-understood, and available dynamic cell cycle reporters do not precisely distinguish all cell cycle stage transitions in vivo . Here we characterized a recently developed improved cell cycle reporter (PIP-FUCCI) that precisely delineates S phase and the S/G2 transition. Live image analysis of primary endothelial cells revealed predicted temporal changes and well-defined stage transitions. A new inducible mouse cell cycle reporter allele was selectively expressed in postnatal retinal endothelial cells upon Cre-mediated activation and predicted endothelial cell cycle status. We developed a semi-automated zonation program to define endothelial cell cycle status in spatially defined and developmentally distinct retinal areas and found predicted cell cycle stage differences in arteries, veins, and remodeled and angiogenic capillaries. Surprisingly, the predicted dearth of proliferative tip cells at the vascular front was accompanied by an unexpected enrichment for endothelial tip cells in G2, suggesting G2 stalling as a contribution to tip-cell arrest. Thus, this improved reporter precisely defines endothelial cell cycle status in vivo and reveals novel G2 regulation that may contribute to unique aspects of blood vessel network expansion.

Background: Endothelial cells regulate their cell cycle as blood vessels remodel and transition to quiescence downstream of blood flow-induced mechanotransduction. Laminar blood flow leads to quiescence, but how flow-mediated quiescence is established and maintained is poorly understood., Methods: Primary human endothelial cells were exposed to laminar flow regimens and gene expression manipulations, and quiescence depth was analyzed via time to cell cycle re-entry after flow cessation. Mouse and zebrafish endothelial expression patterns were examined via scRNA seq analysis, and mutant or morphant fish lacking p27 were analyzed for endothelial cell cycle regulation and in vivo cellular behaviors., Results: Arterial flow-exposed endothelial cells had a distinct transcriptome, and they first entered a deep quiescence, then transitioned to shallow quiescence under homeostatic maintenance conditions. In contrast, venous-flow exposed endothelial cells entered deep quiescence early that did not change with homeostasis. The cell cycle inhibitor p27 ( CDKN1B) was required to establish endothelial flow-mediated quiescence, and expression levels positively correlated with quiescence depth. p27 loss in vivo led to endothelial cell cycle upregulation and ectopic sprouting, consistent with loss of quiescence. HES1 and ID3 , transcriptional repressors of p27 upregulated by arterial flow, were required for quiescence depth changes and the reduced p27 levels associated with shallow quiescence., Conclusions: Endothelial cell flow-mediated quiescence has unique properties and temporal regulation of quiescence depth that depends on the flow stimulus. These findings are consistent with a model whereby flow-mediated endothelial cell quiescence depth is temporally regulated downstream of p27 transcriptional regulation by HES1 and ID3. The findings are important in understanding endothelial cell quiescence mis-regulation that leads to vascular dysfunction and disease., Competing Interests: DECLARATION OF INTERESTS: none

BMP signaling is critical to blood vessel formation and function, but how pathway components regulate vascular development is not well-understood. Here we find that inhibitory SMAD6 functions in endothelial cells to negatively regulate ALK1/ACVRL1-mediated responses, and it is required to prevent vessel dysmorphogenesis and hemorrhage in the embryonic liver vasculature. Reduced Alk1 gene dosage rescued embryonic hepatic hemorrhage and microvascular capillarization induced by Smad6 deletion in endothelial cells in vivo . At the cellular level, co-depletion of Smad6 and Alk1 rescued the destabilized junctions and impaired barrier function of endothelial cells depleted for SMAD6 alone. At the mechanistic level, blockade of actomyosin contractility or increased PI3K signaling rescued endothelial junction defects induced by SMAD6 loss. Thus, SMAD6 normally modulates ALK1 function in endothelial cells to regulate PI3K signaling and contractility, and SMAD6 loss increases signaling through ALK1 that disrupts endothelial junctions. ALK1 loss-of-function also disrupts vascular development and function, indicating that balanced ALK1 signaling is crucial for proper vascular development and identifying ALK1 as a "Goldilocks" pathway in vascular biology regulated by SMAD6.

Fluid shear stress provided by blood flow instigates a transition from active blood vessel network expansion during development, to vascular homeostasis and quiescence that is important for mature blood vessel function. Here we show that SMAD6 is required for endothelial cell flow-mediated responses leading to maintenance of vascular homeostasis. Concomitant manipulation of the mechanosensor Notch1 pathway and SMAD6 expression levels revealed that SMAD6 functions downstream of ligand-induced Notch signaling and transcription regulation. Mechanistically, full-length SMAD6 protein was needed to rescue Notch loss-induced flow misalignment. Endothelial cells depleted for SMAD6 had defective barrier function accompanied by upregulation of proliferation-associated genes and down regulation of junction-associated genes. The vascular protocadherin PCDH12 was upregulated by SMAD6 and required for proper flow-mediated endothelial cell alignment, placing it downstream of SMAD6. Thus, SMAD6 is a required transducer of flow-mediated signaling inputs downstream of Notch1 and upstream of PCDH12, as vessels transition from an angiogenic phenotype to maintenance of a homeostatic phenotype.

Background: Traditional Chinese medicine (TCM) has been shown to be an efficient mode to manage advanced lung cancer, and accurate syndrome differentiation is crucial to treatment. Documented evidence of TCM treatment cases and the progress of artificial intelligence technology are enabling the development of intelligent TCM syndrome differentiation models. This is expected to expand the benefits of TCM to lung cancer patients., Objective: The objective of this work was to establish end-to-end TCM diagnostic models to imitate lung cancer syndrome differentiation. The proposed models used unstructured medical records as inputs to capitalize on data collected for practical TCM treatment cases by lung cancer experts. The resulting models were expected to be more efficient than approaches that leverage structured TCM datasets., Methods: We approached lung cancer TCM syndrome differentiation as a multilabel text classification problem. First, entity representation was conducted with Bidirectional Encoder Representations from Transformers and conditional random fields models. Then, five deep learning-based text classification models were applied to the construction of a medical record multilabel classifier, during which two data augmentation strategies were adopted to address overfitting issues. Finally, a fusion model approach was used to elevate the performance of the models., Results: The F1 score of the recurrent convolutional neural network (RCNN) model with augmentation was 0.8650, a 2.41% improvement over the unaugmented model. The Hamming loss for RCNN with augmentation was 0.0987, which is 1.8% lower than that of the same model without augmentation. Among the models, the text-hierarchical attention network (Text-HAN) model achieved the highest F1 scores of 0.8676 and 0.8751. The mean average precision for the word encoding-based RCNN was 10% higher than that of the character encoding-based representation. A fusion model of the text-convolutional neural network, text-recurrent neural network, and Text-HAN models achieved an F1 score of 0.8884, which showed the best performance among the models., Conclusions: Medical records could be used more productively by constructing end-to-end models to facilitate TCM diagnosis. With the aid of entity-level representation, data augmentation, and model fusion, deep learning-based multilabel classification approaches can better imitate TCM syndrome differentiation in complex cases such as advanced lung cancer., (©Ziqing Liu, Haiyang He, Shixing Yan, Yong Wang, Tao Yang, Guo-Zheng Li. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 16.06.2020.)

Direct cellular reprogramming provides a powerful platform to study cell plasticity and dissect mechanisms underlying cell fate determination. Here, we report a single-cell transcriptomic study of human cardiac (hiCM) reprogramming that utilizes an analysis pipeline incorporating current data normalization methods, multiple trajectory prediction algorithms, and a cell fate index calculation we developed to measure reprogramming progression. These analyses revealed hiCM reprogramming-specific features and a decision point at which cells either embark on reprogramming or regress toward their original fibroblast state. In combination with functional screening, we found that immune-response-associated DNA methylation is required for hiCM induction and validated several downstream targets of reprogramming factors as necessary for productive hiCM reprograming. Collectively, this single-cell transcriptomics study provides detailed datasets that reveal molecular features underlying hiCM determination and rigorous analytical pipelines for predicting cell fate conversion., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Hypertrophic response to pathological stimuli is a complex biological process that involves transcriptional and epigenetic regulation of the cardiac transcriptome. Although previous studies have implicated transcriptional factors and signaling molecules in pathological hypertrophy, the role of RNA-binding protein in this process has received little attention., Methods: Here we used transverse aortic constriction and in vitro cardiac hypertrophy models to characterize the role of an evolutionary conserved RNA-binding protein Lin28a in pathological cardiac hypertrophy. Next-generation sequencing, RNA immunoprecipitation, and gene expression analyses were applied to identify the downstream targets of Lin28a. Epistatic analysis, metabolic assays, and flux analysis were further used to characterize the effects of Lin28a and its downstream mediator in cardiomyocyte hypertrophic growth and metabolic remodeling., Results: Cardiac-specific deletion of Lin28a attenuated pressure overload-induced hypertrophic growth, cardiac dysfunction, and alterations in cardiac transcriptome. Mechanistically, Lin28a directly bound to mitochondrial phosphoenolpyruvate carboxykinase 2 ( Pck2) mRNA and increased its transcript level. Increasing Pck2 was sufficient to promote hypertrophic growth similar to that caused by increasing Lin28a, whereas knocking down Pck2 attenuated norepinephrine-induced cardiac hypertrophy. Epistatic analysis demonstrated that Pck2 mediated, at least in part, the role of Lin28a in cardiac hypertrophic growth. Furthermore, metabolomic analyses highlighted the role for Lin28a and Pck2 in promoting cardiac biosynthesis required for cell growth., Conclusions: Our study demonstrates that Lin28a promotes pathological cardiac hypertrophy and glycolytic reprograming, at least in part, by binding to and stabilizing Pck2 mRNA.

Direct lineage conversion offers a new strategy for tissue regeneration and disease modelling. Despite recent success in directly reprogramming fibroblasts into various cell types, the precise changes that occur as fibroblasts progressively convert to the target cell fates remain unclear. The inherent heterogeneity and asynchronous nature of the reprogramming process renders it difficult to study this process using bulk genomic techniques. Here we used single-cell RNA sequencing to overcome this limitation and analysed global transcriptome changes at early stages during the reprogramming of mouse fibroblasts into induced cardiomyocytes (iCMs). Using unsupervised dimensionality reduction and clustering algorithms, we identified molecularly distinct subpopulations of cells during reprogramming. We also constructed routes of iCM formation, and delineated the relationship between cell proliferation and iCM induction. Further analysis of global gene expression changes during reprogramming revealed unexpected downregulation of factors involved in mRNA processing and splicing. Detailed functional analysis of the top candidate splicing factor, Ptbp1, revealed that it is a critical barrier for the acquisition of cardiomyocyte-specific splicing patterns in fibroblasts. Concomitantly, Ptbp1 depletion promoted cardiac transcriptome acquisition and increased iCM reprogramming efficiency. Additional quantitative analysis of our dataset revealed a strong correlation between the expression of each reprogramming factor and the progress of individual cells through the reprogramming process, and led to the discovery of new surface markers for the enrichment of iCMs. In summary, our single-cell transcriptomics approaches enabled us to reconstruct the reprogramming trajectory and to uncover intermediate cell populations, gene pathways and regulators involved in iCM induction.